test_slab.py

#Common imports
import os
import sys
import numpy as np
import argparse
import copy
import random
import json
import pickle

#Sklearn
import sklearn
from sklearn.manifold import TSNE

#Pytorch
import torch
from torch.autograd import grad
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.autograd import Variable
import torch.utils.data as data_utils

#robustdg
from utils.helper import *
from utils.match_function import *

#slab
from utils.slab_data import *
import utils.scripts.utils  as slab_utils
import utils.scripts.lms_utils as slab_lms_utils

def get_logits(model, loader, device, label=1):
    X, Y = slab_utils.extract_tensors_from_loader(loader)
    L = slab_utils.get_logits_given_tensor(X, model, device=device).detach()
    L = L[Y==label].cpu().numpy()
    S = L[:, 1] - L[:, 0] # compute score / difference to get scalar 
    return S


# Input Parsing
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_name', type=str, default='slab', 
                    help='Datasets: rot_mnist; fashion_mnist; pacs')
parser.add_argument('--method_name', type=str, default='erm_match', 
                    help=' Training Algorithm: erm_match; matchdg_ctr; matchdg_erm')
parser.add_argument('--model_name', type=str, default='slab', 
                    help='Architecture of the model to be trained')
parser.add_argument('--train_domains', nargs='+', type=str, default=["15", "30", "45", "60", "75"], 
                    help='List of train domains')
parser.add_argument('--test_domains', nargs='+', type=str, default=["0", "90"], 
                    help='List of test domains')
parser.add_argument('--out_classes', type=int, default=2, 
                    help='Total number of classes in the dataset')
parser.add_argument('--img_c', type=int, default= 1, 
                    help='Number of channels of the image in dataset')
parser.add_argument('--img_h', type=int, default= 224, 
                    help='Height of the image in dataset')
parser.add_argument('--img_w', type=int, default= 224, 
                    help='Width of the image in dataset')
parser.add_argument('--fc_layer', type=int, default= 1, 
                    help='ResNet architecture customization; 0: No fc_layer with resnet; 1: fc_layer for classification with resnet')
parser.add_argument('--match_layer', type=str, default='logit_match', 
                    help='rep_match: Matching at an intermediate representation level; logit_match: Matching at the logit level')
parser.add_argument('--pos_metric', type=str, default='l2', 
                    help='Cost to function to evaluate distance between two representations; Options: l1; l2; cos')
parser.add_argument('--rep_dim', type=int, default=250, 
                    help='Representation dimension for contrsative learning')
parser.add_argument('--pre_trained',type=int, default=0, 
                    help='0: No Pretrained Architecture; 1: Pretrained Architecture')
parser.add_argument('--perfect_match', type=int, default=1, 
                    help='0: No perfect match known (PACS); 1: perfect match known (MNIST)')
parser.add_argument('--opt', type=str, default='sgd', 
                    help='Optimizer Choice: sgd; adam')
parser.add_argument('--weight_decay', type=float, default=5e-4,
                   help='Weight Decay in SGD')
parser.add_argument('--lr', type=float, default=0.01, 
                    help='Learning rate for training the model')
parser.add_argument('--batch_size', type=int, default=16, 
                    help='Batch size foe training the model')
parser.add_argument('--epochs', type=int, default=15, 
                    help='Total number of epochs for training the model')
parser.add_argument('--penalty_s', type=int, default=-1, 
                    help='Epoch threshold over which Matching Loss to be optimised')
parser.add_argument('--penalty_irm', type=float, default=0.0, 
                    help='Penalty weight for IRM invariant classifier loss')
parser.add_argument('--penalty_aug', type=float, default=1.0, 
                    help='Penalty weight for Augmentation in Hybrid approach loss')
parser.add_argument('--penalty_ws', type=float, default=0.1, 
                    help='Penalty weight for Matching Loss')
parser.add_argument('--penalty_diff_ctr',type=float, default=1.0, 
                    help='Penalty weight for Contrastive Loss')
parser.add_argument('--tau', type=float, default=0.05, 
                    help='Temperature hyper param for NTXent contrastive loss ')
parser.add_argument('--match_flag', type=int, default=0, 
                    help='0: No Update to Match Strategy; 1: Updates to Match Strategy')
parser.add_argument('--match_case', type=float, default=1.0, 
                    help='0: Random Match; 1: Perfect Match. 0.x" x% correct Match')
parser.add_argument('--match_interrupt', type=int, default=5, 
                    help='Number of epochs before inferring the match strategy')
parser.add_argument('--ctr_abl', type=int, default=0, 
                    help='0: Randomization til class level ; 1: Randomization completely')
parser.add_argument('--match_abl', type=int, default=0, 
                    help='0: Randomization til class level ; 1: Randomization completely')
parser.add_argument('--n_runs', type=int, default=3, 
                    help='Number of iterations to repeat the training process')
parser.add_argument('--n_runs_matchdg_erm', type=int, default=1, 
                    help='Number of iterations to repeat training process for matchdg_erm')
parser.add_argument('--ctr_model_name', type=str, default='resnet18', 
                    help='(For matchdg_ctr phase) Architecture of the model to be trained')
parser.add_argument('--ctr_match_layer', type=str, default='logit_match', 
                    help='(For matchdg_ctr phase) rep_match: Matching at an intermediate representation level; logit_match: Matching at the logit level')
parser.add_argument('--ctr_match_flag', type=int, default=1, 
                    help='(For matchdg_ctr phase) 0: No Update to Match Strategy; 1: Updates to Match Strategy')
parser.add_argument('--ctr_match_case', type=float, default=0.01, 
                    help='(For matchdg_ctr phase) 0: Random Match; 1: Perfect Match. 0.x" x% correct Match')
parser.add_argument('--ctr_match_interrupt', type=int, default=5, 
                    help='(For matchdg_ctr phase) Number of epochs before inferring the match strategy')
parser.add_argument('--mnist_seed', type=int, default=0, 
                    help='Change it between 0-6 for different subsets of Mnist and Fashion Mnist dataset')
parser.add_argument('--retain', type=float, default=0, 
                    help='0: Train from scratch in MatchDG Phase 2; 2: Finetune from MatchDG Phase 1 in MatchDG is Phase 2')
parser.add_argument('--cuda_device', type=int, default=0, 
                    help='Select the cuda device by id among the avaliable devices' )
parser.add_argument('--os_env', type=int, default=0, 
                    help='0: Code execution on local server/machine; 1: Code execution in docker/clusters' )


#Differential Privacy
parser.add_argument('--dp_noise', type=int, default=0, 
                    help='0: No DP noise; 1: Add DP noise')
parser.add_argument('--dp_epsilon', type=float, default=1.0, 
                    help='Epsilon value for Differential Privacy')


#MMD, DANN
parser.add_argument('--d_steps_per_g_step', type=int, default=1)
parser.add_argument('--grad_penalty', type=float, default=0.0)
parser.add_argument('--conditional', type=int, default=1)
parser.add_argument('--gaussian', type=int, default=1)


#Slab Dataset
parser.add_argument('--slab_data_dim', type=int, default= 2, 
                    help='Number of features in the slab dataset')
parser.add_argument('--slab_total_slabs', type=int, default=7)
parser.add_argument('--slab_num_samples', type=int, default=1000)
parser.add_argument('--slab_noise', type=float, default=0.1)


#Differentiate between resnet, lenet, domainbed cases of mnist
parser.add_argument('--mnist_case', type=str, default='resnet18', 
                    help='MNIST Dataset Case: resnet18; lenet, domainbed')
parser.add_argument('--mnist_aug', type=int, default=0, 
                    help='MNIST Data Augmentation: 0 (MNIST, FMNIST Privacy Evaluation); 1 (FMNIST)')
 
    
#Multiple random matches
parser.add_argument('--total_matches_per_point', type=int, default=1, 
                    help='Multiple random matches')


# Evaluation specific
parser.add_argument('--test_metric', type=str, default='acc', 
                    help='Evaluation Metrics: acc; match_score, t_sne, mia')
parser.add_argument('--acc_data_case', type=str, default='test', 
                    help='Dataset Train/Val/Test for the accuracy evaluation metric')
parser.add_argument('--top_k', type=int, default=10, 
                    help='Top K matches to consider for the match score evaluation metric')
parser.add_argument('--match_func_aug_case', type=int, default=0, 
                    help='0: Evaluate match func on train domains; 1: Evaluate match func on self augmentations')
parser.add_argument('--match_func_data_case', type=str, default='train', 
                    help='Dataset Train/Val/Test for the match score evaluation metric')
parser.add_argument('--mia_batch_size', default=64, type=int, 
                    help='batch size')
parser.add_argument('--mia_dnn_steps', default=5000, type=int,
                    help='number of training steps')
parser.add_argument('--mia_sample_size', default=1000, type=int,
                    help='number of samples from train/test dataset logits')
parser.add_argument('--mia_logit', default=1, type=int,
                    help='0: Softmax applied to logits; 1: No Softmax applied to logits')
parser.add_argument('--attribute_domain', default=1, type=int, 
                   help='0: spur correlations as attribute; 1: domain as attribute')
parser.add_argument('--adv_eps', default=0.3, type=float,
                    help='Epsilon ball dimension for PGD attacks')
parser.add_argument('--logit_plot_path', default='', type=str,
                    help='File name to save logit/loss plots')

args = parser.parse_args()

#GPU
cuda= torch.device("cuda:" + str(args.cuda_device))
if cuda:
    kwargs = {'num_workers': 1, 'pin_memory': False} 
else:
    kwargs= {}

args.kwargs= kwargs
    
#List of Train; Test domains
train_domains= args.train_domains
test_domains= args.test_domains

#Initialize
final_acc= []
final_auc= []
final_s_auc= []
final_sc_auc= []
base_res_dir=(
                "results/" + args.dataset_name + '/' + args.method_name + '/' + args.match_layer 
                + '/' + 'train_' + str(args.train_domains)  
            )

#TODO: Handle slab noise case in helper functions
if args.dataset_name == 'slab':
    base_res_dir= base_res_dir + '/slab_noise_'  + str(args.slab_noise)

if not os.path.exists(base_res_dir):
    os.makedirs(base_res_dir)    

#Checks
if args.method_name == 'matchdg_ctr' and args.test_metric == 'acc':
    raise ValueError('Match DG during the contrastive learning phase cannot be evaluted for test accuracy metric')
    sys.exit()

if args.perfect_match == 0 and args.test_metric == 'match_score' and args.match_func_aug_case==0:
    raise ValueError('Cannot evalute match function metrics when perfect match is not known')
    sys.exit()
    
#Execute the method for multiple runs ( total args.n_runs )
for run in range(args.n_runs):
    
    #Seed for reproducability
    np.random.seed(10*run) 
    torch.manual_seed(10*run)    
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(10*run)    
    
    #DataLoader        
    train_dataset= torch.empty(0)
    val_dataset= torch.empty(0)
    test_dataset= torch.empty(0)
    if args.test_metric == 'match_score':
        if args.match_func_data_case== 'train':
            train_dataset= get_dataloader( args, run, train_domains, 'train', 1, kwargs )
        elif args.match_func_data_case== 'val':
            val_dataset= get_dataloader( args, run, train_domains, 'val', 1, kwargs )
        elif args.match_func_data_case== 'test':
            test_dataset= get_dataloader( args, run, test_domains, 'test', 1, kwargs )
    elif args.test_metric == 'acc':
        if args.acc_data_case== 'train':
            train_dataset= get_dataloader( args, run, train_domains, 'train', 1, kwargs )
        elif args.acc_data_case== 'test':
            test_dataset= get_dataloader( args, run, test_domains, 'test', 1, kwargs )
    elif args.test_metric in ['mia', 'privacy_entropy', 'privacy_loss_attack']:
        train_dataset= get_dataloader( args, run, train_domains, 'train', 1, kwargs )
        test_dataset= get_dataloader( args, run, test_domains, 'test', 1, kwargs )
    elif args.test_metric == 'attribute_attack':        
        train_dataset= get_dataloader( args, run, train_domains + test_domains, 'train', 1, kwargs )
        test_dataset= get_dataloader( args, run, train_domains + test_domains, 'test', 1, kwargs )        
    else:
        test_dataset= get_dataloader( args, run, test_domains, 'test', 1, kwargs )
        
#     print('Train Domains, Domain Size, BaseDomainIdx, Total Domains: ', train_domains, total_domains, domain_size, training_list_size)
    
    #Import the testing module
    from evaluation.base_eval import BaseEval
    test_method= BaseEval(
                          args, train_dataset, val_dataset,
                          test_dataset, base_res_dir,
                          run, cuda
                         )
    
    test_method.get_model()  
    model= test_method.phi
    
    test_method.get_metric_eval()
    if args.acc_data_case == 'train':        
        std_acc= test_method.metric_score['train accuracy']
    elif args.acc_data_case == 'test':
        std_acc= test_method.metric_score['test accuracy']
    print('Test Accuracy: ', std_acc)

    spur_prob= float(test_domains[0])
    data, temp1, _, _= get_data(args.slab_num_samples, spur_prob, args.slab_noise, args.slab_total_slabs, 'test', run, args.method_name)
    
    # compute standard, S-randomized and S^c-randomized AUC 
    std_auc = slab_utils.get_binary_auc(model, data['te_dl'], cuda)

    # get S-randomized and S^c-randomized datasets 
    s_rand_dl = slab_lms_utils.get_randomized_loader(data['te_dl'], data['W'], [0]) # randomize linear coordinate
    sc_rand_dl = slab_lms_utils.get_randomized_loader(data['te_dl'], data['W'], list(range(1, args.slab_data_dim))) # randomize all slab coordinates

    # compute randomized AUC
    s_rand_auc = slab_utils.get_binary_auc(model, s_rand_dl, cuda) 
    sc_rand_auc = slab_utils.get_binary_auc(model, sc_rand_dl, cuda) 
    
    final_acc.append(std_acc)
    final_auc.append(100*std_auc)
    final_s_auc.append(100*s_rand_auc)
    final_sc_auc.append(100*sc_rand_auc)
#     print ('Standard AUC: {:.3f}'.format(std_auc))
#     print ('Linear-Randomized or S-Randomized AUC: {:.3f}'.format(s_rand_auc))
#     print ('Slabs-Randomized or Sc-Randomized AUC: {:.3f}'.format(sc_rand_auc))    
    
    
    # compute logit scores
#     std_log = get_logits(model, data['te_dl'], cuda)
#     s_rand_log = get_logits(model, s_rand_dl, cuda)
#     sc_rand_log = get_logits(model, sc_rand_dl, cuda)

    # plot logit distributions
#     kw = dict(kde=False, bins=20, norm_hist=True, 
#               hist_kws={"histtype": "step", "linewidth": 2, 
#                         "alpha": 0.8, "ls": '-'})

#     fig, ax = plt.subplots(1,1,figsize=(6,4))
#     ax = sns.distplot(std_log, label='Standard Logits', **kw)
#     ax = sns.distplot(s_rand_log, label=r'$S$-Randomized Logits', **kw)
#     ax = sns.distplot(sc_rand_log, label=r'$S^c$-Randomized Logits', **kw)

#     slab_utils.update_ax(ax, 'Logit Distributions of Data', 'Logits', 'Density', 
#                     ticks_fs=13, label_fs=13, title_fs=16, legend_fs=14, legend_loc='upper left')
#     plt.savefig( 'results/slab_test_logit_plot/' + str(args.method_name)+ '_' + str(args.penalty_ws) + '_' + str(run) + '.jpg')

print(final_sc_auc)
print('Standard Acc', np.mean(final_acc), np.std(final_acc))
print('Standard AUC', np.mean(final_auc), np.std(final_auc))        
print('Linear Randmoized AUC', np.mean(final_s_auc), np.std(final_s_auc))        
print('Slab Randomized AUC', np.mean(final_sc_auc), np.std(final_sc_auc))